R Markdown

Has the COVID-19 pandemic influenced antidepressant prescribing patterns during the winteseason (September-October) across Scottish health boards?

Winter season

scotishcensusgov

#Join the data boards

I loaded september to october data boards from 2017 - 2023 to represent the freshers season then I merged the health boards #I summarise total antidepressant prescriptions per Freshers year and plotted the graph to see the trend #Looked at pre coviid , during covid and after covid trend to see if there has been any impact or association

library(tidyverse)
library(here) # directory stucture
library(gt) # tables
library(janitor) # cleaning data
library(ggplot2) # plotting graph
library(sf) # to read in map data 
library(readxl) # to read in map data
library(plotly) # to make interactive

loading a large amount of data in a shorter time period by downloading and using the mapdfr function (data from 2017-2023)

files <- list.files(here("data", "winter_data"), pattern = "csv")
winter_data <- files %>% 
  map_dfr(~read_csv(here("data", "winter_data", .))) %>% 
clean_names()

clean up data and filter for the sections you want

filtered_winter_data <- winter_data %>% 
filter(str_starts(bnf_item_code,"0403")) %>%  #antidepressant code is 0403
  mutate(year = as.numeric(substr(paid_date_month,1,4)), month = as.numeric(substr(paid_date_month,5,6))) %>% #separates the date into years and month so that i can group winter sections
  mutate(winter_year=case_when(month == 12 ~ year + 1, 
month %in% c(1,2) ~ year) )#makes a new column to group the winter years 

filtered_winter_data <- filtered_winter_data %>% 
  unite("healthboards",hbt2014,hbt,sep = "_")#so some of my data healthboard codes were under the name hbt_2014 AND another was hbt so i had to merge the column so all the healthboard columns fall under one

  filtered_winter_data$healthboards <- gsub("[NA]","",filtered_winter_data$healthboards) 
    filtered_winter_data$healthboards <-
      gsub("_","",filtered_winter_data$healthboards)#had to remove some NA characters and '_' characters

Graph 1

winter_years_data <- filtered_winter_data %>% 
  group_by(winter_year) %>% 
  summarise(total_items=sum(number_of_paid_items,na.rm = TRUE))

plot <- ggplot(winter_years_data, aes(x = winter_year)) +
  geom_col(aes(y = total_items), fill = "lightblue", alpha = 0.7) +   # bar chart
  geom_line(aes(y = total_items), linewidth = 1, colour = "darkblue") +  # line
  geom_point(aes(y = total_items), size = 2, colour = "darkblue") +
  scale_x_continuous(breaks = unique(winter_years_data$winter_year)) +
  labs(
    title = "Winter Antidepressant Prescriptions (Bar + Trend Line)",
    x = "Winter Year",
    y = "Total Antidepressant Prescriptions"
  ) +
  theme_minimal()
ggplotly(plot)
population <- readxl::read_excel(here("data","population.xlsx"), skip=10) %>% 
  clean_names() %>% 
  group_by(x2,all_people) %>% 
summarise () %>% 
  filter(!is.na(all_people))

filtered2_winter_data <- filtered_winter_data %>% 
  group_by(healthboards,bnf_item_code,paid_quantity,winter_year) %>% 
summarise()

filtered2_winter_data <- filtered2_winter_data %>% 
  cross_join(population)

SIMD

SIMD <- readxl::read_excel(here("data","SIMD.xlsx")) %>% 
  clean_names() # loading excel data

questions : not sure the best way to displa my original data ? github - how to get rid of the signs 1- overall national trend (use original graph) 2- i want to show the variation between different regions using healthboards 3- link it to deprevation and look at prescriptions per person 4- can i do a map that shows pre covid and post covid side by side would that count as one 5- voilin plot across differet SIMDs to compare smaller unit of data - gp practice (postcode that links to SIMD) PATCHWORK - MAPS TOGETHETE